Determining changes to autoregulation

ABSTRACT

In some examples, a device includes processing circuitry configured to determine a set of correlation coefficient values for first and second physiological parameters. The processing circuitry is further configured to determine a metric of the correlation coefficient values for a first plurality of bins and for a second plurality of bins, wherein each bin of the first plurality has a first bin parameter and each bin of the second plurality of bins has a second bin parameter different than the first bin parameter. The processing circuitry is also configured to determine a composite estimate of a limit of autoregulation of the patient based on the metric for the first plurality of bins and the metric for the second plurality of bins. The processing circuitry is configured to determine an autoregulation status based on the composite estimate and output, for display via the display, an indication of the autoregulation status.

TECHNICAL FIELD

This disclosure relates to physiological parameter monitoring.

BACKGROUND

Cerebral autoregulation (CA) is the response mechanism by which anorganism regulates cerebral blood flow over a wide range of systemicblood pressure changes through complex myogenic, neurogenic, andmetabolic mechanisms. Autoregulation dysfunction may result from anumber of causes including, stroke, traumatic brain injury, brainlesions, brain asphyxia, or infections of the central nervous system.Intact cerebral autoregulation function occurs over a range of bloodpressures defined between a lower limit of autoregulation (LLA) and anupper limit of autoregulation (ULA).

SUMMARY

This disclosure describes devices, systems, and techniques includingprocessing circuitry configured to determine an estimate of a limit ofautoregulation by at least determining a metric of correlationcoefficient values for each bin of a plurality of bins. The processingcircuitry may define a bin (e.g., a data bin or data bucket) asincluding those correlation coefficient values associated with values ofa physiological parameter within the bin. The processing circuitry mayadjust parameters of the bins such that the particular correlationcoefficient values falling within respective bins varies. Example binparameters may include bin width and separation distance between bins.For example, each bin of the plurality of bins may have a width in termsof a physiological parameter, such as the mean arterial pressure or theoxygen saturation of the patient. The width of each bin may span from aminimum value of the physiological parameter for the bin to a maximumvalue of the physiological parameter for the bin. The processingcircuitry may determine a width of less than five millimeters of mercury(mmHg) for each bin, in some examples, such as when the firstphysiological parameter is mean arterial pressure. The center of a binmay be offset from an adjacent bin by a separation distance defined interms of the first physiological parameter.

The metric associated with a bin may be a value that represents thecorrelation coefficient values in the bin (e.g., a mean, a weightedaverage, and/or a median of the correlation coefficient values). Theprocessing circuitry may determine the metric associated with each binby, for example, determining a mean, weighted average, or a median ofthe correlation coefficients values associated with values of thephysiological parameter between the minimum value and the maximum value.For example, the processing circuitry may determine the metric for a bincentered on 50 mmHg with a width of two mmHg by selecting thecorrelation coefficients values associated with values of thephysiological parameter between 49 and 51 mmHg.

The processing circuitry may also be configured to determine, e.g., forthe same correlation coefficient values determined based on a set ofdata collected over a time period, the metric of the correlationcoefficient values for each bin of a second plurality of bins where eachbin of the second plurality of bins has a bin parameter different thanthe bin parameter of the first plurality of bins. The processingcircuitry can determine a composite estimate of the limit ofautoregulation based on the metric of the correlation coefficient valuesfor each plurality of bins.

Clause 1: In some examples, a device comprises a display and processingcircuitry configured to receive a first signal indicative of a firstphysiological parameter of a patient and a second signal indicative of asecond physiological parameter of the patient. The processing circuitryis also configured to determine a set of correlation coefficient valuesfor a set of values of the first physiological parameter and for a setof values of the second physiological parameter. The processingcircuitry is further configured to determine a metric of the correlationcoefficient values for each bin of a first plurality of bins and foreach bin of a second plurality of bins. Each bin of the first pluralityof bins has a first bin parameter defined in terms of the firstphysiological parameter, each bin of the second plurality of bins has asecond bin parameter defined in terms of the first physiologicalparameter, the second bin parameter being different than the first binparameter. The processing circuitry is configured to determine acomposite estimate of a limit of autoregulation of the patient based onthe metric of the correlation coefficient values for the first pluralityof bins and the metric of the correlation coefficient values for thesecond plurality of bins. The processing circuitry is also configured todetermine an autoregulation status of the patient based on the compositeestimate of the limit of autoregulation and output, for display via thedisplay, an indication of the autoregulation status.

Clause 2: In some examples of clause 1, the processing circuitry isconfigured to determine the composite estimate of the limit ofautoregulation at least in part by determining a first estimate of thelimit of autoregulation based on the metric of the correlationcoefficient values for the first plurality of bins, determining a secondestimate of the limit of autoregulation based on the metric of thecorrelation coefficient values for the second plurality of bins, anddetermining the composite estimate of the limit of autoregulation basedon the first estimate of the limit of autoregulation and the secondestimate of the limit of autoregulation.

Clause 3: In some examples of clause 2, the processing circuitry isconfigured to determine the composite estimate at least in part bydetermining an average of the first estimate of the limit ofautoregulation and the second estimate of the limit of autoregulation.

Clause 4: In some examples of clause 2 or clause 3, the processingcircuitry is configured to determine the composite estimate of the limitof autoregulation at least in part by determining a confidence measurefor the first estimate of the limit of autoregulation and the secondestimate of the limit of autoregulation. The processing circuitry isconfigured to determine the autoregulation status at least in part bydetermining a weighting factor for the composite estimate of the limitof autoregulation based on the confidence measure.

Clause 5: In some examples of clause 4, the processing circuitry isconfigured to determine the weighting factor such that the compositeestimate is weighted higher in a first instance when the first estimateis equal to the second estimate than in a second instance when the firstestimate is not equal to the second estimate.

Clause 6: In some examples of any of clauses 2-5, the limit ofautoregulation is a lower limit of autoregulation, and wherein theprocessing circuitry is configured to determine the composite estimateof the lower limit of autoregulation at least in part by determining alowest value of the first physiological parameter at which the metric ofthe correlation coefficient values is less than a threshold level.

Clause 7: In some examples of any of clauses 1-6, the first binparameter comprises a first width, and the second bin parametercomprises a second width. Each width of the first width and the secondwidth is defined by a difference of a respective minimum value of thefirst physiological parameter and a respective maximum value of thefirst physiological parameter. The processing circuitry is configured todetermine the metric of the correlation coefficient values at least inpart by determining the metric of the correlation coefficient valuesassociated with values of the first physiological parameter in a rangeof greater than the respective minimum value and less than therespective maximum value.

Clause 8: In some examples of any of clauses 1-7, the metric of thecorrelation coefficient values comprises a median of the correlationcoefficient values within the respective bin of the set of bins.

Clause 9: In some examples of any of clauses 1-8, the metric of thecorrelation coefficient values comprises a mean of the correlationcoefficient values within the respective bin of the set of bins.

Clause 10: In some examples of any of clauses 1-9, the first binparameter comprises a first width, the second bin parameter comprises asecond width, and at least one of the first width or the second width isless than or equal to four mmHg.

Clause 11: In some examples of clause 10, wherein at least one of thefirst width or the second width is in a range of greater than or equalto one mmHg and less than or equal to three mmHg.

Clause 12: In some examples of any of clauses 1-11, the first binparameter comprises a first distance by which a center of each bin ofthe first plurality of bins is offset from a center of an adjacent binof the first plurality of bins. The second bin parameter comprises asecond distance by which a center of each bin of the second plurality ofbins is offset from a center of an adjacent bin of the second pluralityof bins, the second distance being different than the first distance.

Clause 13: In some examples of clause 12, at least one of the firstdistance or the second distance is less than or equal to fourmillimeters of mercury mmHg.

Clause 14: In some examples of clause 12 or clause 13, at least one ofthe first distance or the second distance is in a range of greater thanor equal to one millimeters of mercury (mmHg) and less than or equal tothree mmHg.

Clause 15: In some examples of any of clauses 1-14, the processingcircuitry is configured to determine the metric of the correlationcoefficient values for each respective bin of the first plurality ofbins at least in part by determining a first mean and a standarddeviation of the correlation coefficient values for the respective bin,determining an outlier correlation coefficient that is greater thanthree times the standard deviation from the first mean, and determininga second mean of the correlation coefficient values, excluding theoutlier correlation coefficient, for the respective bin.

Clause 16: In some examples, a method comprises receiving, by processingcircuitry and from sensing circuitry, a first signal indicative of afirst physiological parameter of a patient and a second signalindicative of a second physiological parameter of the patient. Themethod also comprises determining, by the processing circuitry, a set ofcorrelation coefficient values for a set of values of the firstphysiological parameter and for a set of values of the secondphysiological parameter. The method further comprises determining, bythe processing circuitry, a metric of the correlation coefficient valuesfor each bin of a first plurality of bins and for each bin of a secondplurality of bins, wherein each bin of the first plurality of bins has afirst bin parameter defined in terms of the first physiologicalparameter, and each bin of the second plurality of bins has a second binparameter defined in terms of the first physiological parameter, thesecond bin parameter being different than the first bin parameter. Themethod comprises determining, by the processing circuitry, a compositeestimate of a limit of autoregulation of the patient based on the metricof the correlation coefficient values for the first plurality of binsand the metric of the correlation coefficient values for the secondplurality of bins. The method also comprises determining, by theprocessing circuitry, an autoregulation status of the patient based onthe composite estimate of the limit of autoregulation and outputting, bythe processing circuitry for display via the display, an indication ofthe autoregulation status.

Clause 17: In some examples, a device comprises a display and processingcircuitry configured to receive a first signal indicative of a firstphysiological parameter of a patient and a second signal indicative of asecond physiological parameter of the patient. The processing circuitryis also configured to determine a set of correlation coefficient valuesfor a set of values of the first physiological parameter and for a setof values of the second physiological parameter. The processingcircuitry is further configured to determine a metric of the correlationcoefficient values for each bin of a first plurality of bins at least inpart by determining a first weighting factor for each correlationcoefficient, where each bin of the first plurality of bins has a firstbin parameter defined in terms of the first physiological parameter. Theprocessing circuitry is also configured to determine a metric of thecorrelation coefficient values for each bin of a second plurality ofbins at least in part by determining a second weighting factor for eachcorrelation coefficient, where each bin of the second plurality of binshas a second bin parameter defined in terms of the first physiologicalparameter, the second bin parameter being different than the first binparameter. The processing circuitry is configured to determine acomposite estimate of a limit of autoregulation of the patient based onthe metric of the correlation coefficient values for the first pluralityof bins and the metric of the correlation coefficient values for thesecond plurality of bins. The processing circuitry is also configured todetermine an autoregulation status of the patient based on the compositeestimate of the limit of autoregulation and output, for display via thedisplay, an indication of the autoregulation status.

Clause 18: In some examples of clause 17, the processing circuitry isconfigured to determine the first weighting factor based on an age ofeach correlation coefficient within the respective bin such that morerecent correlation coefficient values are weighted higher than lessrecent correlation coefficient values. The processing circuitry isconfigured to determine the second weighting factor based on the age ofeach correlation coefficient within the respective bin such that morerecent correlation coefficient values are weighted higher than lessrecent correlation coefficient values.

Clause 19: In some examples of clause 17 or clause 18, the processingcircuitry is configured to determine the first weighting factor based ona distance between a center of a bin of the first plurality of bins anda value of the first physiological parameter associated with therespective correlation coefficient such that correlation coefficientvalues closer to the center of the bin are weighted higher thancorrelation coefficient values farther from the center of the bin. Theprocessing circuitry is configured to determine the second weightingfactor based on a distance between a center of a bin of the secondplurality of bins and the value of the first physiological parameterassociated with the respective correlation coefficient such thatcorrelation coefficient values closer to the center of the bin areweighted higher than correlation coefficient values farther from thecenter of the bin.

Clause 20: In some examples of any of clauses 17-19, the processingcircuitry is configured to determine the first weighting factor based ona signal quality metric. The processing circuitry is configured todetermine the second weighting factor based on the signal qualitymetric.

Clause 21: In some examples, a device comprises sensing circuitryconfigured to receive a first signal indicative of a first physiologicalparameter of a patient and a second signal indicative of a secondphysiological parameter of the patient. The device also comprisesprocessing circuitry configured to determine a set of correlationcoefficient values for a set of values of the first physiologicalparameter and for a set of values of the second physiological parameter.The processing circuitry is further configured to determine a metric ofthe correlation coefficient values for each bin of a first plurality ofbins and for each bin of a second plurality of bins. Each bin of thefirst plurality of bins has a first bin parameter defined in terms ofthe first physiological parameter, each bin of the second plurality ofbins has a second bin parameter defined in terms of the firstphysiological parameter, the second bin parameter being different thanthe first bin parameter. The processing circuitry is configured todetermine a composite estimate of a limit of autoregulation of thepatient based on the metric of the correlation coefficient values forthe first plurality of bins and the metric of the correlationcoefficient values for the second plurality of bins. The processingcircuitry is also configured to determine an autoregulation status ofthe patient based on the composite estimate of the limit ofautoregulation and output, for display via a display, an indication ofthe autoregulation status.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual block diagram illustrating an example regionaloximetry device.

FIG. 2 is a conceptual block diagram illustrating an example regionaloximetry device for monitoring the autoregulation status of a patient.

FIG. 3 illustrates an example graphical user interface includingautoregulation information presented on a display.

FIG. 4 is an example graph illustrating metrics for bins of correlationcoefficient values versus mean arterial pressure.

FIG. 5 is an example graph illustrating the binning of correlationcoefficient values.

FIGS. 6A-6C illustrate two example binning strategies for porcine data.

FIGS. 7A-7G illustrate seven example binning strategies for porcinedata.

FIGS. 8 and 9 are flow diagrams illustrating example techniques fordetermining changes in autoregulation, in accordance with some examplesof this disclosure.

DETAILED DESCRIPTION

This disclosure describes devices, systems, and techniques fordetermining changes in cerebral autoregulation of a patient. A systemmay include a regional oximetry device that includes processingcircuitry configured to determine the cerebral autoregulation status ofthe patient based on a limit of autoregulation, also referred to as alimit of cerebral autoregulation, such as the lower limit ofautoregulation (LLA) and/or the upper limit of autoregulation (ULA). Todetermine the LLA and/or the ULA, the processing circuitry is configuredto determine correlation coefficient values based on two physiologicalparameters. The two physiological parameters may include blood pressure,mean arterial pressure, oxygen saturation, blood volume, and/or anyother physiological parameters.

The processing circuitry may define bin parameters of one or more bins(e.g., data bins or data buckets) that will include those correlationcoefficient values associated with values of a physiological parameterwithin the width of a bin. The bin parameters may be defined in terms ofpre-defined values of the physiological parameters. The processingcircuitry may create each bin as a logical container, defined by binparameters, that holds groups of zero or more correlation coefficientvalues. Each bin can contain a group of correlation coefficient valuesthat are associated with similar values of the first physiologicalparameter. The processing circuitry may define bin parameters (e.g.,width and/or separation distance) for the first plurality of bins suchthat every correlation coefficient falls within one or more bins. Forexample, the processing circuitry may determine a first bin centered ata value of one hundred with a width of ten units (e.g., mmHg). Theprocessing circuitry can determine correlation coefficient valuesassociated with (that fall within) the bin by selecting all of thecorrelation coefficient values associated with values of the firstphysiological parameter between 95 and 105. The processing circuitry maybe configured to determine a metric of the correlation coefficientvalues for each bin of a first plurality of bins and for each bin of asecond plurality of bins.

The processing circuitry may define one or more bin parametersincluding, for example, bin width, distance between bin centers, maximumage of correlation coefficient values, maximum number of correlationcoefficient values per bin, or other bin parameters defining acharacteristic of data bins. For example, each bin of the firstplurality of bins may have a first width defined in terms of one of thetwo physiological parameters, and each bin of the second plurality ofbins may have a second width. The processing circuitry may determinerespective estimates of the limit of autoregulation based on the metricfor the first plurality of bins and based on the metric for the secondplurality of bins. The processing circuitry may then use the first andsecond estimates to determine a composite estimate of the limit ofautoregulation and determine an autoregulation status of a patient basedon the composite estimate. The processing circuitry may be furtherconfigured to output, for display, an indication of the compositeestimate of the limit of autoregulation and/or an indication of theautoregulation status.

The width of the bins or other bin parameters may affect the resultingestimate of the limit of autoregulation. For example, the metrics ofbins with relatively large widths may less accurately show the value ofthe first physiological parameter at which the correlation coefficientvalue decreases below a threshold value. In accordance with thetechniques of this disclosure, the processing circuitry may determinemultiple, different estimates based on pluralities of bins withdifferent widths. The processing circuitry can use the multiple,different estimates to determine a composite estimate that may be moreaccurate than a single estimate. In some examples, if all of theestimates of the limit of autoregulation are relatively close, theprocessing circuitry may determine the composite estimate as the mean orthe median of the estimates and assign a relatively high confidencemeasure to the composite estimate.

In contrast, if the estimates of the limit of autoregulation arerelatively far apart, the processing circuitry may determine a compositeestimate with a relatively low confidence measure indicating that thecomposite estimate may be less accurate. The processing circuitry mayuse the confidence measure to determine the weighting of each estimateon the determination of the composite measure.

In some examples, the composite estimate may be a weighted average ofthe current estimates and previous composite estimates. The processingcircuitry may be configured to weight the current estimates based on theconfidence measure, such that, if all of the current estimates of thelimit of autoregulation are relatively close, the processing circuitrymay weight the current estimates more heavily than if the estimates ofthe limit of autoregulation relatively far apart. The processingcircuitry may “weight” an estimate by determining a weighting factor forthe estimate and multiplying the estimate and the weighting factor todetermine a weighted value.

A patient state, as indicated by sensed physiological signals, maychange relatively rapidly over time. In response to a changing patientstate, some estimates of a limit of autoregulation may change quickly,while other estimates may change slowly. Even if the patient state doesnot change, an inaccurate estimate of the limit of autoregulation canchange rapidly. Processing circuitry that uses multiple, differentestimates can reduce the weighting of outlier estimates and otherinaccurate estimates. The processing circuitry may determine a compositeestimate of the limit of autoregulation that is more accurate, ascompared to processing circuitry determines only one estimate of thelimit of autoregulation based on bins with a width of five millimetersof mercury (mmHg).

The devices, systems, and techniques of this disclosure may increase theaccuracy of the presentation of an estimate of a limit of autoregulationof a patient and the presentation of an indication of the autoregulationstatus of the patient. The presentation of more accurate and more stableinformation may result in increased confidence by a clinician viewingthe presented information, which may lead to more informed decisionmaking by the clinician. A clinician may lose confidence in theinformation presented by the processing circuitry if the information isunstable and/or inaccurate. By using a composite estimate to determinean autoregulation status, the processing circuitry may present moreaccurate autoregulation indications, as compared to another device thatuses a single estimate. By presenting a composite estimate of a limit ofautoregulation, the processing circuitry may generate a simpler andeasier-to-use display for a user, as compared to presenting multipleindividual estimates.

The autoregulation status of a patient may be an indication that thecerebral autoregulation control mechanism of the patient is intact(e.g., functioning properly) or impaired (e.g., not functioningproperly). A cerebral autoregulation control mechanism of the body mayregulate cerebral blood flow (CBF) over a range of systemic bloodpressures. This range of systemic blood pressures may lie within a lowerlimit of autoregulation (LLA) and an upper limit of autoregulation(ULA). Outside of the LLA and the ULA, blood pressure directly drivesCBF, and cerebral autoregulation function may thus be consideredimpaired.

One method to determine the limits of autoregulation (e.g., the LLA andULA) noninvasively using near-infrared spectroscopy (NIRS) technologymay include the COx measure, which is a moving correlation index betweenmean arterial pressure (MAP) and regional oxygen saturation (rSO₂). TheCOx measure (e.g., the Pearson coefficient) is derived from thecorrelation between rSO₂ and MAP. COx relates to the regression line fitor linear correlation between rSO₂ and MAP over a time window having aparticular length, such as three hundred seconds, in some examples. TheCOx method may be used to produce a representation of a patient'sblood-pressure-dependent autoregulation status.

When the cerebral autoregulation is intact for a patient, there istypically no correlation between MAP and rSO₂. In contrast, MAP and rSO₂typically directly correlate (e.g., the correlation index of COx isapproximately 1) when the cerebral autoregulation is impaired. Inpractice, however, sensed data indicative of autoregulation may be noisyand/or there might be a slightly correlated relationship betweenvariables (e.g., MAP and rSO₂) even when cerebral autoregulation isintact for the patient.

Some existing systems for monitoring autoregulation may determine apatient's autoregulation status based on various physiological parametervalues (also referred to herein as physiological values). Suchphysiological values may be subject to various sources of error, such asnoise caused by relative sensor and patient motion, operator error, poorquality measurements, drugs, or other anomalies. However, some existingsystems for monitoring autoregulation may not reduce the various sourcesof error when utilizing the measured physiological values to determinethe patient's autoregulation status. Furthermore, some existing systemsmay not determine and/or utilize a reliable metric to determine whetherthe autoregulation status calculated from the physiological values isreliable. Accordingly, the autoregulation status determined by suchexisting systems may be less accurate or less reliable.

In an intact region of cerebral autoregulation, there may be nocorrelation between these variables whereas in an impaired region ofcerebral autoregulation, the correlation index should approximate unity.In practice, however, the data may be noisy and/or the intact region mayexhibit a slightly positive relationship. This positive relationship mayrender traditional autoregulation limit calculations difficult toperform, resulting in the need for manual interpretation of the datausing arbitrary thresholds. Further, the underlying mathematics of thetechnique may be asymmetric in terms of the results produced forimpaired and intact regions and may be, in fact, not computable for theideal case within the intact region.

A physician may monitor a patient's autoregulation through the use ofvarious monitoring devices and systems that measure variousphysiological parameters. In certain aspects of the present disclosure,a patient's autoregulation may be monitored by correlating measurementsof the patient's blood pressure (e.g., arterial blood pressure) withmeasurements of the patient's oxygen saturation (e.g., regional oxygensaturation). In particular, a cerebral oximetry index (COx) may bederived based at least in part on a linear correlation between thepatient's blood pressure and oxygen saturation. In addition, in certainaspects of the present disclosure, the patient's autoregulation may bemonitored by correlating measurements of the patient's blood pressurewith measurements of the patient's blood volume (e.g., blood volumeproxy). In particular, a hemoglobin volume index (HVx) may be derivedbased at least in part on a linear correlation between the patient'sblood pressure and blood volume.

While features of the present disclosure are discussed with reference toCOx, in other examples, various other linear correlations such as HVxmay be determined to help evaluate a patient's autoregulation status.For example, a linear correlation between measurements of a patient'sblood pressure and measurements of a patient's cerebral blood flow mayderive a mean velocity index (Mx). As a further example, a linearcorrelation between measurements of a patient's blood pressure andmeasurements of a patient's intracranial pressure may derive a pressurereactivity index (PRx). In certain situations, these indexes may beutilized to determine or help evaluate a patient's autoregulation. Thedevices, systems, and techniques of this disclosure can also be appliedto the determination of indices such as HVx, Mx, PRx, and/or any otherindices, coefficients, and correlations. For example, processingcircuitry may be configured to determine a composite estimate of a limitof autoregulation based on a metric for two or more pluralities of binsof HVx indices, Mx indices, or PRx indices.

Additional example details of the physiological parameters that can beused for determining a limit of autoregulation may be found in commonlyassigned U.S. Patent Application Publication No. 2016/0367197 filed onJun. 16, 2016, entitled “Systems and Methods for Reducing Signal NoiseWhen Monitoring Autoregulation,” and commonly assigned U.S. PatentApplication Publication No. 2017/0105631 filed on Oct. 18, 2016,entitled “System and Method for Providing Blood Pressure Safe ZoneIndication During Autoregulation Monitoring,” which are incorporatedherein by reference in their entirety.

FIG. 1 is a conceptual block diagram illustrating an example regionaloximetry device 100. Regional oximetry device 100 includes processingcircuitry 110, memory 120, user interface 130, display 132, sensingcircuitry 140-142, and sensing device(s) 150-152. In some examples,regional oximetry device 100 may be configured to determine and displaythe cerebral autoregulation status of a patient, e.g., during a medicalprocedure or for more long-term monitoring, such as fetal monitoring. Aclinician may receive information regarding the cerebral autoregulationstatus of a patient via display 132 and adjust treatment or therapy tothe patient based on the cerebral autoregulation status information.

Processing circuitry 110, as well as other processors, processingcircuitry, controllers, control circuitry, and the like, describedherein, may include one or more processors. Processing circuitry 110 mayinclude any combination of integrated circuitry, discrete logiccircuitry, analog circuitry, such as one or more microprocessors,digital signal processors (DSPs), application specific integratedcircuits (ASICs), or field-programmable gate arrays (FPGAs). In someexamples, processing circuitry 110 may include multiple components, suchas any combination of one or more microprocessors, one or more DSPs, oneor more ASICs, or one or more FPGAs, as well as other discrete orintegrated logic circuitry, and/or analog circuitry.

Memory 120 may be configured to store measurements of physiologicalparameters, MAP values, rSO₂ values, COx values, and value(s) of an LLAand/or a ULA, for example. Memory 120 may also be configured to storedata such as metrics, bin parameters such as widths and separationdistances between centers of adjacent bins, weighting factors, and/orthreshold levels for metrics. The metrics, widths, separation distancesbetween centers of adjacent bins, weighting factors, and/or thresholdlevels may stay constant throughout the use of device 100 and acrossmultiple patients, or these values may change over time.

In some examples, memory 120 may store program instructions, which mayinclude one or more program modules, which are executable by processingcircuitry 110. When executed by processing circuitry 110, such programinstructions may cause processing circuitry 110 to provide thefunctionality ascribed to it herein. The program instructions may beembodied in software, firmware, and/or RAMware. Memory 120 may includeany volatile, non-volatile, magnetic, optical, or electrical media, suchas a random access memory (RAM), read-only memory (ROM), non-volatileRAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flashmemory, or any other digital media.

User interface 130 and/or display 132 may be configured to presentinformation to a user (e.g., a clinician). User interface 130 and/ordisplay 132 may be configured to present a graphical user interface to auser, where each graphical user interface may include indications ofvalues of one or more physiological parameters of a patient. Forexample, processing circuitry 110 may be configured to present bloodpressure values, physiological parameter values, and indications ofautoregulation status (e.g., cerebral autoregulation status) of apatient via display 132. In some examples, if processing circuitry 110determines that the autoregulation status of the patient is impaired,then processing circuitry 110 may present a notification (e.g., analert) indicating the impaired cerebral autoregulation status viadisplay 132. As another example, processing circuitry 110 may present,via display 132, estimates of rSO₂ for a patient, an estimate of theblood oxygen saturation (SpO₂) determined by processing circuitry 110,pulse rate information, respiration rate information, blood pressure,any other patient parameters, or any combination thereof.

User interface 130 and/or display 132 may include a monitor, cathode raytube display, a flat panel display such as a liquid crystal (LCD)display, a plasma display, a light emitting diode (LED) display, and/orany other suitable display. User interface 130 and/or display 132 may bepart of a personal digital assistant, mobile phone, tablet computer,laptop computer, any other suitable computing device, or any combinationthereof, with a built-in display or a separate display. User interface130 may also include means for projecting audio to a user, such asspeaker(s). Processing circuitry 110 may be configured to present, viauser interface 130, a visual, audible, tactile, or somatosensorynotification (e.g., an alarm signal) indicative of the patient'sautoregulation status and/or a notification indicative of the patient'slimit(s) of autoregulation.

User interface 130 may include or be part of any suitable device forconveying such information, including a computer workstation, a server,a desktop, a notebook, a laptop, a handheld computer, a mobile device,or the like. In some examples, processing circuitry 110 and userinterface 130 may be part of the same device or supported within onehousing (e.g., a computer or monitor). In other examples, processingcircuitry 110 and user interface 130 may be separate devices configuredto communicate through a wired connection or a wireless connection(e.g., communication interface 290 shown in FIG. 2).

Sensing circuitry 140-142 may be configured to receive physiologicalsignals sensed by respective sensing device(s) 150-152 and communicatethe physiological signals to processing circuitry 110. Sensing device(s)150-152 may include any sensing hardware configured to sense aphysiological parameter of a patient, such as, but not limited to, oneor more electrodes, optical receivers, blood pressure cuffs, or thelike. Sensing circuitry 140-142 may convert the physiological signals tousable signals for processing circuitry 110, such that processingcircuitry 110 is configured to receive signals generated by sensingcircuitry 140-142. Sensing circuitry 140-142 may receive signalsindicating physiological parameters from a patient, such as, but notlimited to, blood pressure, regional oxygen saturation, blood volume,heart rate, and respiration. Sensing circuitry 140-142 may include, butare not limited to, blood pressure sensing circuitry, oxygen saturationsensing circuitry, blood volume sensing circuitry, heart rate sensingcircuitry, temperature sensing circuitry, electrocardiography (ECG)sensing circuitry, electroencephalogram (EEG) sensing circuitry, or anycombination thereof. In some examples, sensing circuitry 140-142 and/orprocessing circuitry 110 may include signal processing circuitry such asan analog-to-digital converter.

In some examples, oxygen saturation sensing device 150 is a regionaloxygen saturation sensor configured to generate an oxygen saturationsignal indicative of blood oxygen saturation within the venous,arterial, and/or capillary systems within a region of the patient. Forexample, oxygen saturation sensing device 150 may be configured to beplaced on the patient's forehead and may be used to determine the oxygensaturation of the patient's blood within the venous, arterial, and/orcapillary systems of a region underlying the patient's forehead (e.g.,in the cerebral cortex).

In such cases, oxygen saturation sensing device 150 may include emitter160 and detector 162. Emitter 160 may include at least two lightemitting diodes (LEDs), each configured to emit at different wavelengthsof light, e.g., red or near infrared light. In some examples, lightdrive circuitry (e.g., within sensing device 150, sensing circuitry 140,and/or processing circuitry 110) may provide a light drive signal todrive emitter 160 and to cause emitter 160 to emit light. In someexamples, the LEDs of emitter 160 emit light in the wavelength range ofabout 600 nanometers (nm) to about 1000 nm. In a particular example, oneLED of emitter 160 is configured to emit light at a wavelength of about730 nm and the other LED of emitter 160 is configured to emit light at awavelength of about 810 nm. Other wavelengths of light may also be usedin other examples.

Detector 162 may include a first detection element positioned relatively“close” (e.g., proximal) to emitter 160 and a second detection elementpositioned relatively “far” (e.g., distal) from emitter 160. Lightintensity of multiple wavelengths may be received at both the “close”and the “far” detector 162. For example, if two wavelengths are used,the two wavelengths may be contrasted at each location and the resultingsignals may be contrasted to arrive at a regional saturation value thatpertains to additional tissue through which the light received at the“far” detector passed (tissue in addition to the tissue through whichthe light received by the “close” detector passed, e.g., the braintissue), when it was transmitted through a region of a patient (e.g., apatient's cranium). Surface data from the skin and skull may besubtracted out, to generate a regional oxygen saturation signal for thetarget tissues over time. Oxygen saturation sensing device 150 mayprovide the regional oxygen saturation signal to processing circuitry110 or to any other suitable processing device to enable evaluation ofthe patient's autoregulation status.

In operation, blood pressure sensing device 151 and oxygen saturationsensing device 150 may each be placed on the same or different parts ofthe patient's body. For example, blood pressure sensing device 151 andoxygen saturation sensing device 150 may be physically separate fromeach other and may be separately placed on the patient. As anotherexample, blood pressure sensing device 151 and oxygen saturation sensingdevice 150 may in some cases be part of the same sensor or supported bya single sensor housing. For example, blood pressure sensing device 151and oxygen saturation sensing device 150 may be part of an integratedoximetry system configured to non-invasively measure blood pressure(e.g., based on time delays in a PPG signal) and regional oxygensaturation. One or both of blood pressure sensing device 151 or oxygensaturation sensing device 150 may be further configured to measure otherphysiological parameters, such as hemoglobin, respiratory rate,respiratory effort, heart rate, saturation pattern detection, responseto stimulus such as bispectral index (BIS) or electromyography (EMG)response to electrical stimulus, or the like. While an example regionaloximetry device 100 is shown in FIG. 1, the components illustrated inFIG. 1 are not intended to be limiting. Additional or alternativecomponents and/or implementations may be used in other examples.

Blood pressure sensing device 151 may be any sensor or device configuredto obtain the patient's blood pressure (e.g., arterial blood pressure).For example, blood pressure sensing device 151 may include a bloodpressure cuff for non-invasively monitoring blood pressure or anarterial line for invasively monitoring blood pressure. In certainexamples, blood pressure sensing device 151 may include one or morepulse oximetry sensors. In some such cases, the patient's blood pressuremay be derived by processing time delays between two or morecharacteristic points within a single plethysmography (PPG) signalobtained from a single pulse oximetry sensor.

Additional example details of deriving blood pressure based on acomparison of time delays between certain components of a single PPGsignal obtained from a single pulse oximetry sensor are described incommonly assigned U.S. Patent Application Publication No. 2009/0326386filed Sep. 30, 2008, and entitled “Systems and Methods for Non-InvasiveBlood Pressure Monitoring,” the entire content of which is incorporatedherein by reference. In other cases, the patient's blood pressure may becontinuously, non-invasively monitored via multiple pulse oximetrysensors placed at multiple locations on the patient's body. As describedin commonly assigned U.S. Pat. No. 6,599,251, entitled “ContinuousNon-invasive Blood Pressure Monitoring Method and Apparatus,” the entirecontent of which is incorporated herein by reference, multiple PPGsignals may be obtained from the multiple pulse oximetry sensors, andthe PPG signals may be compared against one another to estimate thepatient's blood pressure. Regardless of its form, blood pressure sensingdevice 151 may be configured to generate a blood pressure signalindicative of the patient's blood pressure (e.g., arterial bloodpressure) over time. Blood pressure sensing device 151 may provide theblood pressure signal to sensing circuitry 141, processing circuitry110, or to any other suitable processing device to enable evaluation ofthe patient's autoregulation status.

Processing circuitry 110 may be configured to receive one or moresignals generated by sensing devices 150-152 and sensing circuitry140-142. The physiological signals may include a signal indicating bloodpressure, a signal indicating oxygen saturation, and/or a signalindicating blood volume of a patient (e.g., an isosbestic signal).Processing circuitry 110 may be configured to determine a set of valuesof a first physiological parameter and a set of values of a secondphysiological parameter based on two or more signals received by sensingdevices 150-152 and sensing circuitry 140-142 and delivered toprocessing circuitry 110. Sensing devices 150-152 and sensing circuitry140-142 can deliver the physiological signals directly to processingcircuitry 110 or sensing circuitry 140-142 can modify the physiologicalsignals (e.g., through pre-processing) before delivering signals toprocessing circuitry 110. The first and second physiological parametersmay include mean arterial pressure, oxygen saturation, and/or bloodvolume. Processing circuitry 110 may associate each value in a set ofvalues with a point in time. For example, processing circuitry 110 maydetermine a value of mean arterial pressure at a particular time basedon the characteristics of a blood pressure signal over a time interval.

Processing circuitry 110 may then be configured to determine a set ofcorrelation coefficient values for the set of values of the firstphysiological parameter and for the set of values of the secondphysiological parameter. Processing circuitry 110 may determine eachcorrelation coefficient for a sample of the values of the firstphysiological parameter and for a sample of the values of the secondphysiological parameter. For example, processing circuitry 110 maydetermine each correlation coefficient based on a Pearson coefficientthat measures the strength and direction of a linear relationshipbetween the values of the first physiological parameter and for a sampleof the values of the second physiological parameter.

Processing circuitry 110 may associate each correlation coefficient witha particular value of the first physiological parameter. As an example,processing circuitry 110 may associate each COx value or each HVx valuewith a particular value of MAP or another physiological parameter, asshown in FIGS. 4, 5, 6A-6C, and 7A-7G. FIG. 5 shows a set of correlationcoefficient values plotted in terms of the associated value of the firstphysiological parameter. The values of the correlation coefficientvalues may range from negative one to positive one.

Processing circuitry 110 is configured to determine a metric of thecorrelation coefficient values for each bin of a plurality of bins (seediscussion of FIGS. 4 and 5 below relating to the formation of bins).Processing circuitry 110 can also determine the width, minimum value,and maximum value of each bin in terms of the first physiologicalparameter. Processing circuitry 110 can also determine the separationdistance between centers of adjacent bins in terms of the firstphysiological parameter. Processing circuitry 110 may be configured todetermine the metric of the correlation coefficient values for each binbased on the correlation coefficient values that fall within the widthof the bin. The metric may be a statistical measure of the values of thecorrelation coefficient values, such as the mean, median, or a weightedaverage.

For example, processing circuitry 110 may determine the following sixCOx values, where the first number in parenthesis is the COx value andthe second number is the associated MAP value, in terms of mmHg:COx₁=(1.0, 60), COx₂=(0.8, 65), COx₃=(0.9, 67), COx₄=(0.4, 68),COx₅=(0.7, 70), and COx₆=(0, 72). Processing circuitry 110 may determinea first bin with width of two mmHg, centered at 66 mmHg, such that thebin has minimum value of 65 mmHg and a maximum value of 67 mmHg.Processing circuitry 110 then determines that the second and third COxvalues fall within the first bin and determines a metric (e.g., mean) of0.85. Processing circuitry 110 can determine a second bin with a widthof two mmHg centered at 68 mmHg. Processing circuitry 110 determines ametric of 0.65 for the second bin based on the third and fourth COxvalues. Processing circuitry 110 can determine a third bin with a widthof two mmHg centered at 70 mmHg. Processing circuitry 110 determines ametric of 0.7 for the third bin based on the fifth COx value. Processingcircuitry 110 may use Equation (1) to determine the mean for the bins,where N equals two for the first and second bins and N equals one forthe third bin.

$\begin{matrix}{{Mean} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{COx}_{i}}}} & (1)\end{matrix}$

Processing circuitry 110 may determine two or more respectivepluralities of bins, where each of the respective pluralities of binsincludes bins having a different bin parameter, such as width orseparation distance between centers of adjacent bins. For example,processing circuitry 110 may determine a width of two mmHg for each binin a first plurality of bins and a width of five mmHg for each bin in asecond plurality of bins. In some examples, processing circuitry 110determines that at least one of the first width or the second width isless than or equal to four mmHg. Processing circuitry 110 can alsodetermine that at least one of the first width or the second width is ina range of greater than or equal to one mmHg and less than or equal tothree mmHg.

In some examples, processing circuitry 110 alternatively or additionallydetermines a different separation distance between centers of adjacentbins in each respective plurality of bins. The separation distance isdefined as the distance between the center of a first bin and the centerof a second bin, where the first and second bins are adjacent (e.g.,there are no other bins positioned between the first and second bins).In some examples, processing circuitry 110 determines that at least oneof the first separation distance or the second separation distance isless than or equal to four mmHg. Processing circuitry 110 can alsodetermine that at least one of the first separation distance or thesecond separation distance is in a range of greater than or equal to onemmHg and less than or equal to three mmHg.

Processing circuitry 110 may determine a separation distance of one mmHgfor the first plurality of bins and a separation distance of two mmHgfor the second plurality of bins. In some examples, processing circuitry110 may determine only a single plurality of bins, where the width ofeach bin in the single plurality is less than five mmHg. Processingcircuitry 110 may also determine a separation distance of less than fivemmHg for the bins of the single plurality. Even using a single pluralityof bins with widths of less than five mmHg, processing circuitry 110 maydetermine a more accurate estimate of a limit of autoregulation, ascompared to another device using a single type of bin with widths and aseparation distance between centers of adjacent bins of five mmHg.

A smaller separation distance between centers of adjacent bins mayresult in more bins over a range of values of the first physiologicalparameter. If the range of values of MAP is forty mmHg, a separationdistance of five mmHg results in approximately eight bins, whereas aseparation distance of one mmHg results in approximately forty bins.Thus, a smaller separation distance may be more processing intensive forprocessing circuitry 110 but the smaller separation distance may alsoproduce more accurate results.

Processing circuitry 110 may determine an estimate of a limit ofautoregulation based on the metric of the correlation coefficient valuesfor each plurality of bins. The metric of the correlation coefficientvalues may be near positive one for bins centered at very low values andvery high values of the first physiological parameter (see, e.g., FIG.4). Therefore, in order to determine an estimate of the lower limit ofautoregulation, processing circuitry 110 may determine the lowest valueof the first physiological parameter that is associated with a bin witha metric below a threshold level, such as 0.8, 0.7, 0.6, 0.5, 0.4, 0.3,0.2, 0.1, or 0.0. In order to determine an estimate of the upper limitof autoregulation, processing circuitry 110 may determine the highestvalue of the first physiological parameter that is associated with a binwith a metric below a threshold level. FIG. 4 shows an example ofmetrics near positive one at very high MAP values and very low MAPvalues and metrics less than a threshold level between the limits ofautoregulation.

In some examples, processing circuitry 110 is further configured todetermine a composite estimate of the limit of autoregulation of thepatient based on the metric of the correlation coefficient values forthe first plurality of bins and the metric of the correlationcoefficient values for the second plurality of bins. Processingcircuitry 110 can determine the composite estimate as the mean of theestimates of the limit of autoregulation based on each plurality ofbins. Processing circuitry 110 may be configured to exclude outlierestimates from the determination of the composite estimate. In someexamples, processing circuitry 110 may be configured to determine thecomposite estimate as a weighted average of a previous compositeestimate and each current estimate of the limit of autoregulation (e.g.,a first estimate based on a first plurality of bins, a second estimatebased on a second plurality of bins, etc.).

In some examples, processing circuitry 110 may be configured todetermine a confidence measure based on the first estimate and thesecond estimate, where the confidence measure can indicate the relativedistance or closeness of the two estimates. For example, if the twoestimates are close in value, processing circuitry 110 may determine arelatively high confidence measure that indicates a higher likelihoodthat the two estimates are accurate. Processing circuitry 110 maydetermine the confidence measure based on previous estimates in order todetermine rapid changes in the estimates of the limit of autoregulation.Rapid changes in the estimates may indicate inaccuracies. In someexamples, processing circuitry 110 can also determine estimates of thelimit of autoregulation based on other indices and other autoregulationmetrics (e.g., COx, HVx, PRx, Mx, etc.) and then determine theconfidence measure based on all of the estimates. Processing circuitry110 may use the confidence measure to determine the composite estimateof the limit of autoregulation by determining a weighted average of theestimates, including other estimates and previous estimates. A higherconfidence measure may result in processing circuitry 110 using a higherweighting factor for one or both of the estimates of the limit ofautoregulation.

In some examples, within each bin, processing circuitry 110 may weighteach correlation coefficient by applying a weighting factor. Forexample, processing circuitry 110 may determine the weighting factor fora correlation coefficient based on the age of the correlationcoefficient, such that more recent correlation coefficient values areweighted higher or more heavily than less recent correlation coefficientvalues. Weighting by age may result in a composite estimate that adaptsmore quickly to changes in autoregulation. Processing circuitry 110 mayweight the data within a bin by the age of each correlation coefficient.For example, processing circuitry 110 may apply a down weighting toolder data to better detect recent changes.

In some examples, processing circuitry 110 may alternatively oradditionally determine the weighting factor for a correlationcoefficient based on the distance of the correlation coefficient fromthe center of the bin, such that correlation coefficient values that arecloser to the center are weighted higher or more heavily thancorrelation coefficient values that are farther from the center.Processing circuitry 110 may weight the data within a bin by thedistance from each correlation coefficient to the center of the bin. Forexample, processing circuitry 110 may use a triangular function orGaussian distribution (see Equations (3) and (4) below). Weighting bydistance may result in metrics that more accurately represent the valueof the correlation coefficient values at the center of each bin.

In some examples, processing circuitry 110 may alternatively oradditionally determine the weighting factor for a correlationcoefficient based on a signal quality metric for the correlationcoefficient. Weighting by signal quality metric may cause processingcircuitry 110 to use higher-quality correlation coefficient values andexclude or dampen the effects of lower-quality correlation coefficientvalues. Processing circuitry 110 may also weight the data within a binby the determined quality using a signal quality metric computedconcurrently when computing the data point.

Each plurality of bins may have a different bin parameter, where a widthof each bin and a separation distance between centers of adjacent binsare examples of bin parameters. Other examples of bin parameters includethe maximum age of the correlation coefficient values in a bin and themaximum number of correlation coefficient values in a bin. For example,a first plurality of bins may have a maximum age of one hundred seconds,two hundred seconds, or three hundred seconds, or any other length oftime. Thus, to determine a metric for a bin, processing circuitry 110may also use only the correlation coefficient values in the bin that aremore recent than the maximum age. Processing circuitry 110 can determinea different maximum age for a second plurality of bins.

Processing circuitry 110 can also determine different maximum (and/orminimum) number of correlation coefficient values for each plurality ofbins. Processing circuitry 110 can apply the maximum or minimum numberto use only recent correlation coefficient values for a bin unless thebin has very few recent correlation coefficient values. That is,processing circuitry may include the most recent correlation coefficientvalues up to a maximum number of values, and exclude the remaining oldercorrelation coefficient values from the bin. If the bin has very fewrecent correlation coefficient values, processing circuitry 110 caninclude older values, in addition to the more recent values, if the morerecent values are insufficient to meet the minimum number of correlationcoefficient values. In some examples, processing circuitry 110 may usecorrelation coefficient values outside of the default width parameter tomeet the minimum number of correlation coefficient values.

The bin parameters need not be constant across time or across values ofthe first physiological parameter. Processing circuitry 110 may beconfigured to dynamically change a bin parameter for a plurality of binsover time. For example, processing circuitry 110 may shorten the maximumage in response to a trigger event. Processing circuitry may beconfigured to determine bin parameters based on values of the firstphysiological parameter. For example, processing circuitry 110 can use asmaller width and/or smaller separation distance for bins between thelimits of autoregulation (e.g., for intact autoregulation statuses)because there may be more correlation coefficient values available forbins centered between the limits of autoregulation.

In some examples, processing circuitry 110 is also configured todetermine an autoregulation status of the patient based on the compositeestimate of the limit of autoregulation. In the example of a compositeestimate of the lower limit of autoregulation, processing circuitry 110may determine whether the current mean arterial pressure of the patientis greater than the composite estimate of the lower limit ofautoregulation. If the current mean arterial pressure is greater thanthe composite estimate of the lower limit of autoregulation, thenprocessing circuitry 110 can determine that the patient has intactautoregulation, unless the current mean arterial pressure is greaterthan the upper limit of autoregulation. By determining a compositeestimate based on two estimates of the limit of autoregulation using atleast two pluralities of bins, processing circuitry 110 may moreaccurately determine autoregulation status, as compared to anotherdevice that does not implement the techniques of this disclosure.

Processing circuitry 110 outputs, such as for display via display 132 ofuser interface 130, an indication of the autoregulation status. Display132 may present a graphical user interface such as graphical userinterface 300 shown in FIG. 3. As described in further detail below,graphical user interface 300 includes an indicator of autoregulationstatus 350. The indication of autoregulation status may include text,colors, and/or audio presented to a user. Processing circuitry 110 maybe further configured to present an indication of one or more limits ofautoregulation (e.g., indicators 360 and 370).

Although other example techniques are possible, regional oximetry device100 may be configured to determine the first estimate of the limit ofautoregulation based on COx values derived from MAP values and rSO₂values. Alternatively, processing circuitry 110 may determine the firstestimate of the limit of autoregulation based on HVx values, BVS values,and/or rSO₂ values. Regional oximetry device 200 of FIG. 2 includesadditional detail on how processing circuitry 110 can determine rSO₂values based on a physiological signal received from sensing device 150.

FIG. 2 is a conceptual block diagram illustrating an example regionaloximetry device 200 for monitoring the autoregulation status of apatient. In the example shown in FIG. 2, regional oximetry device 200 iscoupled to sensing device 250 and may be collectively referred to as aregional oximetry system, which each generate and process physiologicalsignals of a subject. In some examples, sensing device 250 and regionaloximetry device 200 may be part of an oximeter. As shown in FIG. 2,regional oximetry device 200 includes back-end processing circuitry 214,user interface 230, light drive circuitry 240, front-end processingcircuitry 216, control circuitry 245, and communication interface 290.Regional oximetry device 200 may be communicatively coupled to sensingdevice 250. Regional oximetry device 200 is an example of regionaloximetry device 100 shown in FIG. 1. In some examples, regional oximetrydevice 200 may also include a blood pressure sensor and/or a bloodvolume sensor (e.g., sensing devices 151 and 152 shown in FIG. 1).

In the example shown in FIG. 2, sensing device 250 includes light source260, detector 262, and detector 263. In some examples, sensing device250 may include more than two detectors. Light source 260 may beconfigured to emit photonic signals having two or more wavelengths oflight (e.g., red and infrared (IR)) into a subject's tissue. Forexample, light source 260 may include a red light emitting light sourceand an IR light emitting light source, (e.g., red and IR light emittingdiodes (LEDs)), for emitting light into the tissue of a subject togenerate physiological signals. In some examples, the red wavelength maybe between about 600 nm and about 700 nm, and the IR wavelength may bebetween about 800 nm and about 1000 nm. Other wavelengths of light maybe used in other examples. Light source 260 may include any number oflight sources with any suitable characteristics. In examples in which anarray of sensors is used in place of sensing device 250, each sensingdevice may be configured to emit a single wavelength. For example, afirst sensing device may emit only a red light while a second sensingdevice may emit only an IR light. In some examples, light source 260 maybe configured to emit two or more wavelengths of near-infrared light(e.g., wavelengths between 600 nm and 1000 nm) into a subject's tissue.In some examples, light source 260 may be configured to emit fourwavelengths of light (e.g., 724 nm, 770 nm, 810 nm, and 850 nm) into asubject's tissue. In some examples, the subject may be a medicalpatient.

As used herein, the term “light” may refer to energy produced byradiative sources and may include one or more of ultrasound, radio,microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray orX-ray electromagnetic radiation. Light may also include any wavelengthwithin the radio, microwave, infrared, visible, ultraviolet, or X-rayspectra, and that any suitable wavelength of electromagnetic radiationmay be appropriate for use with the present techniques. Detectors 262and 263 may be chosen to be specifically sensitive to the chosentargeted energy spectrum of light source 260.

In some examples, detectors 262 and 263 may be configured to detect theintensity of multiple wavelengths of near-infrared light. In someexamples, detectors 262 and 263 may be configured to detect theintensity of light at the red and IR wavelengths. In some examples, anarray of detectors may be used and each detector in the array may beconfigured to detect an intensity of a single wavelength. In operation,light may enter detector 262 after passing through the subject's tissue,including skin, bone, and other shallow tissue (e.g., non-cerebraltissue and shallow cerebral tissue). Light may enter detector 263 afterpassing through the subject's tissue, including skin, bone, othershallow tissue (e.g., non-cerebral tissue and shallow cerebral tissue),and deep tissue (e.g., deep cerebral tissue). Detectors 262 and 263 mayconvert the intensity of the received light into an electrical signal.The light intensity may be directly related to the absorbance and/orreflectance of light in the tissue. That is, when more light at acertain wavelength is absorbed or reflected, less light of thatwavelength is received from the tissue by detectors 262 and 263.

After converting the received light to an electrical signal, detectors262 and 263 may send the detection signals to regional oximetry device200, where the detection signals may be processed and physiologicalparameters may be determined (e.g., based on the absorption of the redand IR wavelengths in the subject's tissue at both detectors). In someexamples, one or more of the detection signals may be preprocessed bysensing device 250 before being transmitted to regional oximetry device200. Additional example details of determining oxygen saturation basedon light signals may be found in commonly assigned U.S. Pat. No.9,861,317, which issued on Jan. 9, 2018, and is entitled “Methods andSystems for Determining Regional Blood Oxygen Saturation,” the entirecontent of which is incorporated herein by reference.

Control circuitry 245 may be coupled to light drive circuitry 240,front-end processing circuitry 216, and back-end processing circuitry214, and may be configured to control the operation of these components.In some examples, control circuitry 245 may be configured to providetiming control signals to coordinate their operation. For example, lightdrive circuitry 240 may generate one or more light drive signals, whichmay be used to turn on and off light source 260, based on the timingcontrol signals provided by control circuitry 245. Front-end processingcircuitry 216 may use the timing control signals to operatesynchronously with light drive circuitry 240. For example, front-endprocessing circuitry 216 may synchronize the operation of ananalog-to-digital converter and a demultiplexer with the light drivesignal based on the timing control signals. In addition, the back-endprocessing circuitry 214 may use the timing control signals tocoordinate its operation with front-end processing circuitry 216.

Light drive circuitry 240, as discussed above, may be configured togenerate a light drive signal that is provided to light source 260 ofsensing device 250. The light drive signal may, for example, control theintensity of light source 260 and the timing of when light source 260 isturned on and off. In some examples, light drive circuitry 240 providesone or more light drive signals to light source 260. Where light source260 is configured to emit two or more wavelengths of light, the lightdrive signal may be configured to control the operation of eachwavelength of light. The light drive signal may comprise a single signalor may comprise multiple signals (e.g., one signal for each wavelengthof light).

Front-end processing circuitry 216 may perform any suitable analogconditioning of the detector signals. The conditioning performed mayinclude any type of filtering (e.g., low pass, high pass, band pass,notch, or any other suitable filtering), amplifying, performing anoperation on the received signal (e.g., taking a derivative, averaging),performing any other suitable signal conditioning (e.g., converting acurrent signal to a voltage signal), or any combination thereof. Theconditioned analog signals may be processed by an analog-to-digitalconverter of circuitry 216, which may convert the conditioned analogsignals into digital signals. Front-end processing circuitry 216 mayoperate on the analog or digital form of the detector signals toseparate out different components of the signals. Front-end processingcircuitry 216 may also perform any suitable digital conditioning of thedetector signals, such as low pass, high pass, band pass, notch,averaging, or any other suitable filtering, amplifying, performing anoperation on the signal, performing any other suitable digitalconditioning, or any combination thereof. Front-end processing circuitry216 may decrease the number of samples in the digital detector signals.In some examples, front-end processing circuitry 216 may also removedark or ambient contributions to the received signal.

Back-end processing circuitry 214 may include processing circuitry 210and memory 220. Processing circuitry 210 may include an assembly ofanalog or digital electronic components and may be configured to executesoftware, which may include an operating system and one or moreapplications, as part of performing the functions described herein withrespect to, e.g., processing circuitry 110. Processing circuitry 210 mayreceive and further process physiological signals received fromfront-end processing circuitry 216. For example, processing circuitry210 may determine one or more physiological parameter values based onthe received physiological signals. For example, processing circuitry210 may compute one or more of regional oxygen saturation, blood oxygensaturation (e.g., arterial, venous, or both), pulse rate, respirationrate, respiration effort, blood pressure, hemoglobin concentration(e.g., oxygenated, deoxygenated, and/or total), any other suitablephysiological parameters, or any combination thereof.

Processing circuitry 210 may perform any suitable signal processing of asignal, such as any suitable band-pass filtering, adaptive filtering,closed-loop filtering, any other suitable filtering, and/or anycombination thereof. Processing circuitry 210 may also receive inputsignals from additional sources not shown. For example, processingcircuitry 210 may receive an input signal containing information abouttreatments provided to the subject from user interface 230. Additionalinput signals may be used by processing circuitry 210 in any of thedeterminations or operations it performs in accordance with back-endprocessing circuitry 214 or regional oximetry device 200.

Processing circuitry 210 is an example of processing circuitry 110 andis configured to perform the techniques of this disclosure. For example,processing circuitry 210 is configured to receive signals indicative ofphysiological parameters. Processing circuitry 210 is also configured todetermine correlation coefficient values and a metric for each bin of aplurality of bins, where each bin includes one or more correlationcoefficient values. Processing circuitry 210 may be configured todetermine a composite estimate based on the determined metrics.Processing circuitry 210 is also configured to determine anautoregulation status based on a composite estimate of a limit ofautoregulation.

Memory 220 may include any suitable computer-readable media capable ofstoring information that can be interpreted by processing circuitry 210.In some examples, memory 220 may store bin parameters, correlationcoefficient values, determined metrics, composite estimates, referenceabsorption curves, reference sets, determined values, such as bloodoxygen saturation, pulse rate, blood pressure, fiducial point locationsor characteristics, initialization parameters, any other determinedvalues, or any combination thereof, in a memory device for laterretrieval. Back-end processing circuitry 214 may be communicativelycoupled with user interface 230 and communication interface 290.

User interface 230 may include input device 234, display 232, andspeaker 236. User interface 230 is an example of user interface 130shown in FIG. 1, and display 232 is an example of display 132 shown inFIG. 1. User interface 230 may include, for example, any suitable devicesuch as one or more medical devices (e.g., a medical monitor thatdisplays various physiological parameters, a medical alarm, or any othersuitable medical device that either displays physiological parameters oruses the output of back-end processing 214 as an input), one or moredisplay devices (e.g., monitor, personal digital assistant (PDA), mobilephone, tablet computer, clinician workstation, any other suitabledisplay device, or any combination thereof), one or more audio devices,one or more memory devices, one or more printing devices, any othersuitable output device, or any combination thereof.

Input device 234 may include any type of user input device such as akeyboard, a mouse, a touch screen, buttons, switches, a microphone, ajoy stick, a touch pad, or any other suitable input device orcombination of input devices. In other examples, input device 234 may bea pressure-sensitive or presence-sensitive display that is included aspart of display 232. Input device 234 may also receive inputs to selecta model number of sensing device 250, blood pressure sensor 250 (FIG.2), or blood pressure processing equipment. In some examples, processingcircuitry 210 may determine a width and/or separation distance betweencenters of adjacent bins for each plurality of bins based on user inputreceived from input device 234. Thus, a user may be able to select,using input device 234, widths and/or separation distances for eachplurality of bins.

In some examples, the subject may be a medical patient and display 232may exhibit a list of values which may generally apply to the subject,such as, for example, an oxygen saturation signal indicator, a bloodpressure signal indicator, a COx signal indicator, a COx valueindicator, and/or an autoregulation status indicator. Display 232 mayalso be configured to present additional physiological parameterinformation. Graphical user interface 300 shown in FIG. 3 is an exampleof an interface that can be presented via display 232 of FIG. 2.Additionally, display 232 may present, for example, one or moreestimates of a subject's regional oxygen saturation generated byregional oximetry device 200 (referred to as an “rSO₂” measurement).Display 232 may also present indications of the upper and lower limitsof autoregulation. Speaker 236 within user interface 230 may provide anaudible sound that may be used in various examples, such as for example,sounding an audible alarm in the event that a patient's physiologicalparameters are not within a predefined normal range.

Communication interface 290 may enable regional oximetry device 200 toexchange information with external devices. Communication interface 290may include any suitable hardware, software, or both, which may allowregional oximetry device 200 to communicate with electronic circuitry, adevice, a network, a server or other workstations, a display, or anycombination thereof. For example, regional oximetry device 200 mayreceive MAP values and/or oxygen saturation values from an externaldevice via communication interface 290.

The components of regional oximetry device 200 that are shown anddescribed as separate components are shown and described as such forillustrative purposes only. In some examples the functionality of someof the components may be combined in a single component. For example,the functionality of front end processing circuitry 216 and back-endprocessing circuitry 214 may be combined in a single processor system.Additionally, in some examples the functionality of some of thecomponents of regional oximetry device 200 shown and described hereinmay be divided over multiple components. For example, some or all of thefunctionality of control circuitry 245 may be performed in front endprocessing circuitry 216, in back-end processing circuitry 214, or both.In other examples, the functionality of one or more of the componentsmay be performed in a different order or may not be required. In someexamples, all of the components of regional oximetry device 200 can berealized in processor circuitry.

FIG. 3 illustrates an example graphical user interface 300 includingautoregulation information presented on a display. FIG. 3 is an exampleof a presentation by processing circuitry 110 on display 132 shown inFIG. 1 or by processing circuitry 210 on display 232 shown in FIG. 2.Graphical user interface 300 may be configured to display variousinformation related to blood pressure, oxygen saturation, the COx index,limits of autoregulation, and/or autoregulation status. As shown,graphical user interface 300 may include oxygen saturation signalindicator 310, blood pressure signal indicator 320, and COx signalindicator 330. Graphical user interface 300 may include COx valueindicator 340, autoregulation status indicator 350, and limit ofautoregulation indicators 360 and 370.

Blood pressure signal indicator 320 may present a set of MAP valuesdetermined by processing circuitry 110 of regional oximetry device 100.In some examples, blood pressure signal indicator 320 may present MAPvalues as discrete points over time or in a table. Blood pressure signalindicator 320 may also present MAP values as a moving average orwaveform of discrete points. Blood pressure signal indicator 320 maypresent MAP values as a single value (e.g., a number) representing acurrent MAP value. Oxygen saturation signal indicator 310 and COx signalindicator 330 may also present rSO2 values and COx values, respectively,as discrete points, in a table, as a moving average, as a waveform,and/or as a single value.

COx signal indicator 330 may present a set of correlation coefficientvalues determined by processing circuitry 110. Processing circuitry 110may determine the correlation coefficient values as a function of theoxygen saturation values presented in oxygen saturation signal indicator310 and the MAP values presented in blood pressure signal indicator 320.In some examples, a COx value at or near one (unity) indicates theautoregulation status of a patient is impaired, as shown inautoregulation status indicator 350.

Processing circuitry 110 may determine a set of correlation coefficientvalues and associated values of a first physiological parameter usingthe values presented in indicators 310, 320, and/or 330. Processingcircuitry 110 may determine a COx value and the associated value of thefirst physiological parameter (e.g., MAP or rSO₂) for a particular time(e.g., 2,550 seconds in the graphs of indicators 320 and 330). This COxvalue may be an “unbinned COx value,” and processing circuitry 110 canplot the unbinned COx value along with the associated MAP value. FIGS. 5and 6A depict unbinned correlation coefficient values plotted with theassociated MAP values.

Processing circuitry 110 may be configured to determine and store, inmemory 120, a set of correlation coefficient values, e.g., as atwo-dimensional array. Each row of the array may be a separate entry(e.g., a COx value and an associated MAP value). The first column of thearray may be the MAP values presented in blood pressure signal indicator320. The second column of the array may be the COx values presented inCOx signal indicator 330. Processing circuitry 110 can plot each entryof the array as shown in FIGS. 5 and 6A, with the MAP value being thevalue along the horizontal axis and the COx value being the value alongthe vertical axis.

COx value indicator 340 shows a COx value of 0.8, which may result in adetermination by processing circuitry 110 that the autoregulation statusof the patient is impaired. Processing circuitry 110 may be configuredto present, as the COx value in COx value indicator 340, the mostrecently determined COx value or a moving average of recently determinedCOx values. In order to determine the autoregulation status of a patientfor presentation in autoregulation status indicator 350, processingcircuitry 110 may determine whether the most recent MAP value shown inblood pressure signal indicator 320 is between the limits ofautoregulation presented in limit of autoregulation indicators 360 and370.

Processing circuitry 110 may present limit of autoregulation indicators360 and/or 370 in terms of blood pressure, for example, mmHg. Processingcircuitry 110 can determine the limits of cerebral autoregulation (LLAand ULA) for presentation in indicators 360 and 370 based on arelationship between the blood pressure of a patient and anotherphysiological parameter of the patient. For example, indicator 360 maybe highlighted when the LLA has been exceeded or indicator 360 may behighlighted when the ULA has been exceeded. In other examples, a singleindicator may present the type of limit that has been exceed by the MAPvalue. If the LLA or ULA change, processing circuitry 110 may controluser interface 300 to change the value of the LLA or ULA in accordancewith any change to that respective value.

Processing circuitry 110 may determine a composite estimate of a lowerlimit of autoregulation presented in indicator 360 and/or a compositeestimate of an upper limit of autoregulation presented in indicator 370.Processing circuitry 110 may be configured to generate a notification inresponse to determining that the MAP value is less than or equal to thecomposite average of the lower limit of autoregulation. Processingcircuitry 110 may output the notification in autoregulation statusindicator 350 as text, color, blinking, and/or any other suitablevisible or audible manner.

FIG. 4 is an example graph illustrating metrics for bins of correlationcoefficient values versus mean arterial pressure. Each of symbols410A-410J depicts the metric (e.g., an average correlation coefficientvalue) of each bin (e.g., at the center of a circle) along with an errorbar. In some examples, the error bar represents the largest and smallestvalues of the correlation coefficient values in the respective bin. Insome examples, the error bar represents the standard deviation and/orpercentiles of the correlation coefficient values in the bin. AlthoughFIGS. 4-9 are described with respect to processing circuitry 110 ofregional oximetry device 100 (FIG. 1), in other examples, processingcircuitry 210, 214, and/or 216 (FIG. 2), alone or in combination withprocessing circuitry 110, may perform any part of the techniques ofFIGS. 4-9. The graph of FIG. 4 depicts COx values plotted against MAPvalues, but processing circuitry 110 can use other physiologicalparameters to determine a limit of autoregulation. For example,processing circuitry 110 may use brain-cerebral perfusion pressure (CPP)and cerebral blood flow (CBF) to determine the Mx measure.

A strong positive correlation may exist between MAP and rSO₂ in regionsof autoregulatory impairment, hence the COx values may tend to a valueof unity, as shown by symbols 410A-410C and 410H-410J. Regions of intactautoregulation, however, may produce no correlation between rSO₂ andchanges in MAP and hence the COx values may be near or below zero in theintact region, as shown by symbols 410D-410G. In this ideal case, a stepchange shown by symbols 410A-410J may occur when transitioning fromintact to impaired regions at LLA 420 or ULA 422. In practice, however,the binned data may be generally noisy and processing circuitry 110 mayuse a COx threshold level somewhere between zero and positive one todifferentiate the correlating and non-correlating portions of the plot.For example, processing circuitry 110 may use a threshold level of 0.0,0.1, 0.2, 0.3, 0.4, or 0.5 to determine a limit of autoregulation.

The bins associated with symbols 410A-410J may be part of a firstplurality of bins, where each of the bins associated with symbols410A-410J has a width and a separation distance 430 between centers ofadjacent bins. Each of symbols 410A-410J is depicted to include an upperhorizontal line, a lower horizontal line, and a middle circle. The upperhorizontal line may represent the highest COx value in the bin, and thelower horizontal line may represent the lowest COx value in the bin. Insome examples, the horizontal lines indicate standard deviations and/orpercentiles of the correlation coefficient values in a bin. The centerof the circle may represent the value of the metric (e.g., mean,weighted average, or median) for the bin. Processing circuitry 110 maybe configured to remove, exclude, or not use outlier values of thecorrelation coefficient values to determine the highest COx value, thelowest COx value, and/or the value of the metric.

Processing circuitry 110 can determine or derive a COx value from thecorrelation between rSO₂ and MAP. Processing circuitry 110 can determinethe COx value as the Pearson coefficient relating to the regression linefit between rSO₂ and MAP over a 300-second window. Processing circuitry110 can use the COx method to produce a picture of the patient'sblood-pressure-dependent autoregulation status. The COx plot shown inFIG. 4 may exhibit a drop in typical values when transitioning frompressures below the LLA (e.g., limit 420) to the intact region ofautoregulation and similarly, at higher pressures, exhibits a stepincrease when transitioning from the intact region of autoregulation topressures above the ULA (e.g., limit 422). Processing circuitry 110 mayplot the data by binning the COx values in non-overlapping bins at fivemmHg intervals (e.g., separation distances). In addition, each bin maybe five mmHg wide. FIG. 4 illustrates plotting the mean of thecorrelation coefficient values in each bin with an associated error bar,as shown in FIG. 4.

FIG. 5 is an example graph illustrating the binning of correlationcoefficient values. The graph of FIG. 5 depicts twenty-six determinedcorrelation coefficient values, but in other examples there may be moreor fewer correlation coefficient values determined. For example,processing circuitry 110 may be configured to determine one or morecorrelation coefficient values every second. Processing circuitry 110may use a window of three hundred seconds such that there are hundredsof correlation coefficient values for the determination of metrics of aplurality of bins.

The graph of FIG. 5 includes a single bin with width 530. Width 530 isdefined by the difference between maximum value 522 and minimum value520. Width 530 is defined in terms of the first physiological parameter,which may be MAP, rSO₂, HVS, BVS, etc. If processing circuitry 110 usesMAP as the first physiological parameter, processing circuitry 110 candetermine width 530 in terms of mmHg, such as one mmHg, two mmHg, threemmHg, four mmHg, five mmHg, and/or any other suitable width. AlthoughFIG. 5 depicts only one bin, processing circuitry 110 may be configuredto determine a plurality of bins across a range of the firstphysiological parameter.

The graph of FIG. 5 depicts eleven correlation coefficient values in thebin bounded by maximum value 522 and minimum value 520. Processingcircuitry 110 may be configured to determine a metric for the bin atleast in part by determining the metric of the correlation coefficientvalues associated with values of the first physiological parameter(e.g., MAP) in a range of greater than minimum value 520 and less thanmaximum value 522 (e.g., the eleven correlation coefficient valuespositioned between values 520 and 522). In some examples, processingcircuitry 110 determines a metric for the bin by determining mean 550 ofthe eleven correlation coefficient values.

Additionally or alternatively, processing circuitry 110 can determine ametric for the bin by excluding outlier correlation coefficients (e.g.,outlier correlation coefficient 540) and determining a mean of theremaining coefficient values (e.g., determining mean 552 of theremaining ten correlation coefficient values). Processing circuitry 110can determine mean 550 and a standard deviation of the elevencorrelation coefficient values for the bin. Processing circuitry 110 canthen determine that correlation coefficient 540 is greater than threetimes the standard deviation from mean 550. For example, processingcircuitry may determine that mean 550 is equal to 0.2 and that thestandard deviation is equal to 0.3. In response to determining thatcorrelation coefficient 540 is equal to −0.8, processing circuitry 110determines that correlation coefficient 540 is greater than three timesthe standard deviation from mean 550. In some examples, thedetermination of an outlier may be based on a different factor, such astwo times or four times the standard deviation. Processing circuitry 110then determines mean 552, excluding correlation coefficient 540, for thebin. Thus, processing circuitry 110 may exclude any outliers in a binbefore calculating the metric for the bin in order to mitigate againstnoisy values.

Mean 552 may be a more accurate estimate of the mean correlationcoefficient between values 520 and 522 because correlation coefficient540 may be an outlier caused by temporary patient events such ascatherization of the patient, probe (e.g., sensor) movement relative tothe patient, or line flushing, and should be disregarded. Excludingcorrelation coefficient 540 from the determination of mean 552 mayresult in a more accurate determination of the metric for the bin and,consequently, a more accurate determination of an estimate of a limit ofautoregulation and an autoregulation status.

$\begin{matrix}{{{Weighted}\mspace{14mu} {average}} = {\frac{1}{\sum\limits_{i = 1}^{N}A_{i}}{\sum\limits_{i = 1}^{N}( {A_{i} \times {COx}_{i}} )}}} & (2)\end{matrix}$

In some examples, processing circuitry 110 may determine a metric for abin that is a weighted average (e.g., a weighted mean) of thecorrelation coefficient values in the bin. Processing circuitry 110 mayuse Equation (2) to determine a weighted average using a weightingfactor for each correlation coefficient. In some examples, processingcircuitry 110 may determine a weighting factor (A_(i) in Equations(2)-(4)) for each correlation coefficient, where each weighting factormay be based on a characteristic of the respective correlationcoefficient.

For example, the characteristic can be the distance from the center ofthe bin to the value of the first physiological parameter associatedwith the correlation coefficient (MAP_(i)), as shown in Equations (3)and (4). According to Equations (3) and (4), correlation coefficientvalues closer to the center of the bin (MAP_(center)) are weightedhigher than correlation coefficient values farther from the center ofthe bin. Processing circuitry 110 may use Equation (3) to determine aweighting factor based on a linear (e.g., triangular) function, wherethe weighting factor is equal to one for a correlation coefficientpositioned at the center of a bin and equal to zero for a correlationcoefficient positioned at the edge of a bin. Processing circuitry 110may use Equation (4) to determine a weighting factor based on a Gaussiandistribution function.

$\begin{matrix}{A_{i} = \frac{{{MAP}_{i} - {MAP}_{center}}}{{bin}\mspace{14mu} {width}}} & (3) \\{A_{i} = {\frac{1}{\sigma \sqrt{2\; \pi}}e^{- \frac{{({{MAP}_{i} - {MAP}_{center}})}^{2}}{2\; \sigma^{2}}}}} & (4)\end{matrix}$

Processing circuitry 110 can alternatively or additionally determine theweighting factors on the age of the correlation coefficient, such thatprocessing circuitry 110 assigns higher weighting factors to more recentcorrelation coefficient values, as compared to less recent correlationcoefficient values. Processing circuitry 110 can also determine theweighting factor based on a signal quality metric, such that processingcircuitry 110 assigns higher weighting factors to correlationcoefficient values based on higher quality signals, as compared tocorrelation coefficient values based on lower quality signals.Processing circuitry 110 may determine the quality of a correlationcoefficient based on the characteristics of signals received byprocessing circuitry 110 from sensing devices 150-152 and sensingcircuitry 140-142. These characteristics include signal strength,variability, noise level, and/or any other signal characteristics thatindicate the quality of the signal, such as a signal-to-noise ratio.Drop outs in the signals (e.g., time periods without any data) receivedby processing circuitry 110 can be indicators of poor quality in therespective signal. Processing circuitry 110 may be configured to comparea correlation coefficient value to previously determined correlationcoefficient values in a range of similar blood pressures to determine ifthe correlation coefficient value is an outlier.

Processing circuitry 110 may use weighting factors to improve themetrics determined for each bin. For example, processing circuitry 110may use weighting factors to filter out unreliable correlationcoefficient values or less representative correlation coefficientvalues. Further, processing circuitry 110 may use weighting factors todetermine a metric that is representative of the center of the bin,rather than the edges of the bin.

FIGS. 6A-6C illustrate two example binning strategies for porcine data.FIG. 6A illustrates unbinned correlation coefficient values. Lowerlimits of autoregulation 600A, 600B, and 600C represent a visualestimate of the separation between the intact region of autoregulationwhere COx values vary from −1 to +1 and the impaired region ofautoregulation where COx values are often only at or near +1. Lowerlimits of autoregulation 600A, 600B, and 600C are near a MAP value of 82mmHg.

FIG. 6B illustrates a first plurality of bins having a first width and afirst separation distance between centers of adjacent bins. FIG. 6Cillustrates a second plurality of bins having a second width and asecond separation distance between centers of adjacent bins. In someexamples, the first width equals five mmHg and the first separationdistance equals five mmHg for FIG. 6B, and the second width equals twommHg and the second separation distance equals one mmHg for FIG. 6C.

Processing circuitry 110 may be configured to determine a first estimateof the lower limit of autoregulation based on the metric of thecorrelation coefficient values for the first plurality of bins depictedin FIG. 6B. Processing circuitry 110 may determine the first estimate ofthe lower limit of autoregulation as illustrated by the bin at whicharrow 610B is pointing, i.e., the bin centered at 85 mmHg. Processingcircuitry 110 may determine the first estimate of the lower limit ofautoregulation by moving from left to right, from impaired to intact, tofind the first bin at which the metric is less than a threshold level.For example, the metric for the bins shown in FIG. 6B does not dropsignificantly until the bin centered at 85 mmHg. Processing circuitry110 may use an algorithm (e.g., finding the lowest bin having a metricbelow a threshold level) to determine the lower limit of autoregulation.Using a separation distance of five mmHg in FIG. 6B, processingcircuitry 110 may not be able to determine the first estimate of thelower limit of autoregulation to a resolution finer than five mmHg.Thus, processing circuitry 110 may only be able to determine the firstestimate of the lower limit of autoregulation at 80 mmHg or 85 mmHg, andnot at 81, 82, 83, or 84 mmHg. There may be significantly more variationbetween the metrics of adjacent bins when using a larger separationdistance, as compared to the variation between the metrics of adjacentbins when using a smaller separation distance.

Processing circuitry 110 may be configured to determine a secondestimate of the lower limit of autoregulation based on the metric of thecorrelation coefficient values for the second plurality of bins depictedin FIG. 6C, which is a more fine-grained approach than the firstplurality of bins shown in FIG. 6B. Processing circuitry 110 maydetermine the second estimate of the lower limit of autoregulation asillustrated by the bin at which arrow 610C is pointing, i.e., the bincentered at 82 mmHg. Processing circuitry 110 may determine the secondestimate of the lower limit of autoregulation by moving from left toright, from impaired to intact, to find the first bin at which themetric has dropped below a threshold level (e.g., jumped downwards).Using a separation distance of one mmHg in FIG. 6C, processing circuitry110 may be able to determine the first estimate of the lower limit ofautoregulation to a resolution of one mmHg. Thus, processing circuitry110 may be able to determine the first estimate of the lower limit ofautoregulation at 80, 81, 82, 83, 84, or 85 mmHg based on the metric forthe bins centered at each MAP value. Using a smaller separation distanceresults in more bins on either side of lower limit of autoregulation600C and much better resolution than FIG. 6B.

Processing circuitry 110 may determine a more accurate estimate of alimit of autoregulation (e.g., much better detection) based on thesecond plurality of bins, where each bin has a smaller width and asmaller separation distance than the first plurality of bins shown inFIG. 6B. The smaller width of the second plurality of bins may result inbetter resolution of the metrics determined by processing circuitry 110.

Processing circuitry 110 may be further configured to determine acomposite estimate of the lower limit of autoregulation based on thefirst estimate and the second estimate. Processing circuitry 110 maydetermine composite estimate based on a mean, median, average, and/orweighted average of the first estimate and the second estimate (e.g.,83.5 mmHg). The use of first and second estimates is an example, and inother examples, processing circuitry 110 may determine a compositeestimate based on more than two estimates. Processing circuitry 110 maythen be configured to determine an autoregulation status of a patientbased on the composite estimate of the limit of autoregulation bydetermining whether the most recent MAP value for the patient is lessthan or equal to the composite estimate. Processing circuitry 110 maysimultaneously use several different combinations of bin locations andbin widths to determine an estimate of a limit of autoregulation.Processing circuitry 110 may determine an average of the resultingestimates to produce a single value (e.g., a composite estimate), andprocessing circuitry 110 may use the level of agreement between valuesas a confidence measure.

FIGS. 7A-7G illustrates seven example binning strategies for porcinedata. Each graph of FIGS. 7A-7G depicts a plurality of bins determinedby processing circuitry 110. The text above each graph of FIGS. 7A-7Gshows the width and separation distance between centers of adjacent binsfor each plurality of bins. In general, processing circuitry 110 willdetermine a more accurate estimate of a limit of autoregulation using asmaller width and/or smaller separation distance, although greateraccuracy is sometimes possible with larger widths and/or largerseparation distances. By using a smaller separation distance, processingcircuitry 110 can determine, with finer granularity, greater resolution,and greater precision, a step-down or step-up in metrics that occursnear limits of autoregulation.

By using a smaller separation distance, processing circuitry 110 mayconsume more processing resources, as compared to using a largerseparation distance. In addition, processing circuitry 110 may use moreprocessing resources to determine metrics for two or more pluralities ofbins than to determine metrics for only one plurality of bins. However,processing circuitry 110 may have sufficient processing resourcesavailable to determine metrics for many pluralities of bins havingrelatively small separation distances.

In some examples, processing circuitry 110 is configured to determine ametric for a first plurality of bins having a first separation distancebetween centers of adjacent bins. Processing circuitry 110 may also beconfigured to determine a metric for a second plurality of bins having asecond separation distance between centers of adjacent bins. In someexamples, the first and second pluralities of bins may have the samewidths. Thus, processing circuitry 110 may use different values for oneor more bin parameter for each pluralities of bins and use the samevalues for other bin parameter(s) for the pluralities of bins. The binparameters include one or more of width, separation distance betweencenters of adjacent bins, algorithms used to determine weightingfactors, maximum age of correlation coefficient values, maximum numberof correlation coefficient values, and/or any other suitable binparameters.

In some examples, processing circuitry 110 determines two or moreestimates of a limit of autoregulation that are not equal. Processingcircuitry 110 may determine a first estimate of the limit ofautoregulation based on a first plurality of bins and a second estimateof the limit of autoregulation based on a second plurality of bins.Although a plurality of bins with a smaller width and/or smallerseparation distance may generally result in a more accurate estimate, aplurality of bins with a larger width and/or larger separation distancemay sometimes result in a more accurate estimate. Thus, if processingcircuitry 110 uses two or more pluralities of bins, rather than only oneplurality of bins, the resulting composite estimate may generally be amore accurate estimate of the limit of autoregulation, as compared to anestimate based on only one plurality of bins. Moreover, processingcircuitry 110 may more quickly determine and report changes in a limitof autoregulation, relative to when the changes actually occur in apatient, using two or more pluralities of bins.

FIGS. 8 and 9 are flow diagrams illustrating example techniques fordetermining changes in autoregulation, in accordance with some examplesof this disclosure. Although FIGS. 8 and 9 are described with respect toprocessing circuitry 110 of regional oximetry device 100 (FIG. 1), inother examples, processing circuitry 210, 214, and/or 216 (FIG. 2),alone or in combination with processing circuitry 110, may perform anypart of the techniques of FIGS. 8 and 9.

In the example of FIG. 8, processing circuitry 110 receives a firstsignal indicative of a first physiological parameter of a patient and asecond signal indicative of a second physiological parameter of thepatient (800). In some examples, processing circuitry 110 receivessignals directly from sensing devices 150-152. Additionally oralternatively, processing circuitry 110 receives signals from sensingcircuitry 140-142, where sensing circuitry 140-142 may pre-process thephysiological signals before delivering the signals to processingcircuitry 110.

In the example of FIG. 8, processing circuitry 110 determines a set ofcorrelation coefficient values for a set of values of the firstphysiological parameter and for a set of values of the secondphysiological parameter (802). Processing circuitry 110 may determineeach correlation coefficient, for example, as a Pearson's coefficientfor the two physiological parameters. Each correlation coefficient valuemay indicate the linear correlation between the two physiologicalparameters at a particular time or over a particular window of time. Insome examples, processing circuitry 110 may determine the linearcorrelation between the two physiological parameters over a window offive seconds or ten seconds.

In the example of FIG. 8, processing circuitry 110 determines a metricof the correlation coefficient values for each bin of a first pluralityof bins, wherein each bin of the first plurality of bins has a first binparameter defined in terms of the first physiological parameter (804).Processing circuitry 110 then determines a metric of the correlationcoefficient values for each bin of a second plurality of bins, whereineach bin of the second plurality of bins has a second bin parameterdefined in terms of the first physiological parameter that is differentthan the first bin parameter (806). The bin parameter can include one ormore of the width of each bin, the separation distance between adjacentbins, and/or the algorithms used to determine weighting factors for thecorrelation coefficient values in each bin. In some examples, processingcircuitry 110 may determine the metric of the correlation coefficientvalues for only one plurality of bins, where the plurality of bins has awidth of less than five mmHg and a separation distance of less than fivemmHg to provide for better resolution.

Processing circuitry 110 may use a mean, median, weighted average, orany other metric as the metric for each of the bins. If the firstphysiological parameter is mean arterial pressure, then the widths foreach plurality of bins may be one mmHg, two mmHg, three mmHg, four mmHg,five mmHg, or any other suitable width. Processing circuitry 110 mayoptimize the width of the bins for the detection of a limit ofautoregulation by changing the bin parameters including the width of thebins or the separation distance between bin centers. For example,processing circuitry 110 may use ten mmHg wide bins, separated by fivemmHg which may be more robust to noise in individual bins. Processingcircuitry 110 may use a smaller separation such as one mmHg with a widthof two mmHg to find a more granular estimate of the limit ofautoregulation.

In the example of FIG. 8, processing circuitry 110 determines acomposite estimate of a limit of autoregulation of the patient based onthe metric of the correlation coefficient values for the first pluralityof bins and the metric of the correlation coefficient values for thesecond plurality of bins (808). Processing circuitry 110 may firstdetermine an estimate of the limit of autoregulation for each pluralityof bins and then determine the composite estimate based on the estimatesfor each of the plurality of bins. Processing circuitry 110 maydetermine the composite estimate based on an average of the estimatesfor each plurality of bins. Alternatively or additionally, processingcircuitry 110 can determine the composite estimate based on previousestimates, as discussed with respect to FIG. 9.

In the example of FIG. 8, processing circuitry 110 determines anautoregulation status of the patient based on the composite estimate ofthe limit of autoregulation (810). Processing circuitry 110 candetermine the autoregulation status at least in part by determiningwhether the current mean arterial pressure value of the patient isgreater than the composite estimate of the lower limit of autoregulationand/or less than the composite estimate of the upper limit ofautoregulation. Processing circuitry 110 outputs an indication of theautoregulation status for display via display 132 (812). Processingcircuitry 110 can cause display 132 to present one or more of theindicators 310, 320, 330, 340, 350, 360, and 370. For example,processing circuitry 110 can cause display 132 to present autoregulationstatus indicator 350, which may include text or a color representativeof an impaired or intact autoregulation status.

Processing circuitry 110 may also use the techniques of this disclosureto determine the composite estimate based on HVx values, PRx values,and/or Mx values, rather than just COx values, using the binningtechniques described herein. For example, in step 802, processingcircuitry 110 can determine a set of HVx values, PRx values, and/or Mxvalues and then determine a metric of the HVx values, PRx values, and/orMx values for a plurality of bins. Processing circuitry 110 may use thetechniques of this disclosure to determine a composite estimate of alimit of autoregulation based on PRx values for use where one of sensingdevices 150-152 includes an intracranial pressure (ICP) probe.

In the example of FIG. 9, processing circuitry 110 determines sets ofvalues for a first physiological parameter and a second physiologicalparameter (900 and 902). For example, processing circuitry 110 maydetermine a set of MAP values and a set of oxygen saturation valuesbased on two signals received by processing circuitry 110 (see, e.g.,indicators 310 and 320 shown in the FIG. 3). Processing circuitry 110determines a set of correlation coefficient values based on the valuesof the first and second physiological parameters (904).

In the example of FIG. 9, processing circuitry 110 determines a metricof the correlation coefficient values for each bin of two or morepluralities of bins (906A and 906B). Processing circuitry 110 thendetermines individual estimates of a limit of autoregulation for each ofthe two or more pluralities of bins (908A and 908B). Processingcircuitry 110 determines a composite estimate of the limit ofautoregulation based on the individual estimates determined from eachplurality of bins (910). The composite estimate may be a mean of theindividual estimates or a weighted average of the individual estimates,where processing circuitry can determine the weighting of eachindividual estimate based on the width and/or separation distance of thecorresponding plurality of bins.

In the example of FIG. 9, processing circuitry 110 determines anautoregulation status of the patient based on the composite estimate ofthe limit of autoregulation (912). Processing circuitry 110 mayoptionally determine a confidence measure based on the individualestimates (914), and processing circuitry 110 may use the confidencemeasure to determine the autoregulation status. Processing circuitry 110may determine the confidence measure based on the difference between theindividual estimates and/or on the difference between the individualestimates and a previous composite estimate. The confidence measure mayindicate a measure of the reliability of the individual estimates andthe current composite estimate.

In response to determining that there is a relatively large differencebetween the individual estimates, processing circuitry 110 may determinea confidence measure indicating relatively low reliability for thecurrent composite measure. In response to determining that a confidencemeasure indicating relatively low reliability, processing circuitry 110may weight the current composite estimate less heavily, causing aprevious composite measure to have a higher weighting. In some examples,processing circuitry 110 determines a final estimate of the limit ofautoregulation based on a weighted average of the current compositeestimate and the previous iteration of the final estimate of the limitof autoregulation. Processing circuitry 110 can assign a lower weightingfactor to the current composite estimate based on determining aconfidence measure that indicates that the current composite estimate isless reliable. Processing circuitry 110 can determine the weightingfactor for the composite estimate such that the composite estimate isweighted higher when the first estimate is equal to the second estimate,as compared to when the first estimate is not equal to the secondestimate. In this way, processing circuitry 110 can determine theautoregulation status by determining a confidence measure for theindividual estimates and then determining a weighting factor for thecomposite estimate of the limit of autoregulation based on theconfidence measure. Processing circuitry then outputs, for display viadisplay 132, an indication of the composite estimate of the limit ofautoregulation and/or an indication of the autoregulation status (916).

The disclosure contemplates computer-readable storage media comprisinginstructions to cause a processor to perform any of the functions andtechniques described herein. The computer-readable storage media maytake the example form of any volatile, non-volatile, magnetic, optical,or electrical media, such as a RAM, ROM, NVRAM, EEPROM, or flash memory.The computer-readable storage media may be referred to asnon-transitory. A programmer, such as patient programmer or clinicianprogrammer, or other computing device may also contain a more portableremovable memory type to enable easy data transfer or offline dataanalysis.

The techniques described in this disclosure, including those attributedto devices 100 and 200, processing circuitry 110, 210, 214, and 216,memories 120 and 220, displays 132 and 232, sensing circuitries 140-142,circuitries 240 and 245, sensing devices 150-152 and 250, and variousconstituent components, may be implemented, at least in part, inhardware, software, firmware or any combination thereof. For example,various aspects of the techniques may be implemented within one or moreprocessors, including one or more microprocessors, DSPs, ASICs, FPGAs,or any other equivalent integrated or discrete logic circuitry, as wellas any combinations of such components, embodied in programmers, such asphysician or patient programmers, stimulators, remote servers, or otherdevices. The term “processor” or “processing circuitry” may generallyrefer to any of the foregoing logic circuitry, alone or in combinationwith other logic circuitry, or any other equivalent circuitry.

As used herein, the term “circuitry” refers to an ASIC, an electroniccircuit, a processor (shared, dedicated, or group) and memory thatexecute one or more software or firmware programs, a combinational logiccircuit, or other suitable components that provide the describedfunctionality. The term “processing circuitry” refers one or moreprocessors distributed across one or more devices. For example,“processing circuitry” can include a single processor or multipleprocessors on a device. “Processing circuitry” can also includeprocessors on multiple devices, wherein the operations described hereinmay be distributed across the processors and devices.

Such hardware, software, firmware may be implemented within the samedevice or within separate devices to support the various operations andfunctions described in this disclosure. For example, any of thetechniques or processes described herein may be performed within onedevice or at least partially distributed amongst two or more devices,such as between devices 100 and 200, processing circuitry 110, 210, 214,and 216, memories 120 and 220, sensing circuitries 140-142, and/orcircuitries 240 and 245. In addition, any of the described units,modules or components may be implemented together or separately asdiscrete but interoperable logic devices. Depiction of differentfeatures as modules or units is intended to highlight differentfunctional aspects and does not necessarily imply that such modules orunits must be realized by separate hardware or software components.Rather, functionality associated with one or more modules or units maybe performed by separate hardware or software components, or integratedwithin common or separate hardware or software components.

The techniques described in this disclosure may also be embodied orencoded in an article of manufacture including a non-transitorycomputer-readable storage medium encoded with instructions. Instructionsembedded or encoded in an article of manufacture including anon-transitory computer-readable storage medium encoded, may cause oneor more programmable processors, or other processors, to implement oneor more of the techniques described herein, such as when instructionsincluded or encoded in the non-transitory computer-readable storagemedium are executed by the one or more processors. Examplenon-transitory computer-readable storage media may include RAM, ROM,programmable ROM (PROM), erasable programmable ROM (EPROM),electronically erasable programmable ROM (EEPROM), flash memory, a harddisk, a compact disc ROM (CD-ROM), a floppy disk, a cassette, magneticmedia, optical media, or any other computer readable storage devices ortangible computer readable media.

In some examples, a computer-readable storage medium comprisesnon-transitory medium. The term “non-transitory” may indicate that thestorage medium is not embodied in a carrier wave or a propagated signal.In certain examples, a non-transitory storage medium may store data thatcan, over time, change (e.g., in RAM or cache). Elements of devices andcircuitry described herein, including, but not limited to, devices 100and 200, processing circuitry 110, 210, 214, and 216, memories 120 and220, displays 132 and 232, sensing circuitries 140-142, circuitries 240and 245, sensing devices 150-152 and 250 may be programmed with variousforms of software. The one or more processors may be implemented atleast in part as, or include, one or more executable applications,application modules, libraries, classes, methods, objects, routines,subroutines, firmware, and/or embedded code, for example.

Where processing circuitry 110 is described herein as determining that avalue is less than or equal to another value, this description may alsoinclude processing circuitry 110 determining that a value is only lessthan the other value. Similarly, where processing circuitry 110 isdescribed herein as determining that a value is less than another value,this description may also include processing circuitry 110 determiningthat a value is less than or equal to the other value. The sameproperties may also apply to the terms “greater than” and “greater thanor equal to.”

Various examples of the disclosure have been described. Any combinationof the described systems, operations, or functions is contemplated.These and other examples are within the scope of the following claims.

What is claimed is:
 1. A device comprising: a display; and processingcircuitry configured to: receive a first signal indicative of a firstphysiological parameter of a patient; receive a second signal indicativeof a second physiological parameter of the patient; determine a set ofcorrelation coefficient values for a set of values of the firstphysiological parameter and for a set of values of the secondphysiological parameter; determine a metric of the correlationcoefficient values for each bin of a first plurality of bins, whereineach bin of the first plurality of bins has a first bin parameterdefined in terms of the first physiological parameter; determine themetric of the correlation coefficient values for each bin of a secondplurality of bins, wherein each bin of the second plurality of bins hasa second bin parameter defined in terms of the first physiologicalparameter, the second bin parameter being different than the first binparameter; determine a composite estimate of a limit of autoregulationof the patient based on the metric of the correlation coefficient valuesfor the first plurality of bins and the metric of the correlationcoefficient values for the second plurality of bins; determine anautoregulation status of the patient based on the composite estimate ofthe limit of autoregulation; and output, for display via the display, anindication of the autoregulation status.
 2. The device of claim 1,wherein the processing circuitry is configured to determine thecomposite estimate of the limit of autoregulation at least in part by:determining a first estimate of the limit of autoregulation based on themetric of the correlation coefficient values for the first plurality ofbins; determining a second estimate of the limit of autoregulation basedon the metric of the correlation coefficient values for the secondplurality of bins; and determining the composite estimate of the limitof autoregulation based on the first estimate of the limit ofautoregulation and the second estimate of the limit of autoregulation.3. The device of claim 2, wherein the processing circuitry is configuredto determine the composite estimate at least in part by determining anaverage of the first estimate of the limit of autoregulation and thesecond estimate of the limit of autoregulation.
 4. The device of claim2, wherein the processing circuitry is configured to determine thecomposite estimate of the limit of autoregulation at least in part bydetermining a confidence measure for the first estimate of the limit ofautoregulation and the second estimate of the limit of autoregulation,and wherein the processing circuitry is configured to determine theautoregulation status at least in part by determining a weighting factorfor the composite estimate of the limit of autoregulation based on theconfidence measure.
 5. The device of claim 4, wherein the processingcircuitry is configured to determine the weighting factor such that thecomposite estimate is weighted higher in a first instance when the firstestimate is equal to the second estimate than in a second instance whenthe first estimate is not equal to the second estimate.
 6. The device ofclaim 2, wherein the limit of autoregulation is a lower limit ofautoregulation, and wherein the processing circuitry is configured todetermine the composite estimate of the lower limit of autoregulation atleast in part by determining a lowest value of the first physiologicalparameter at which the metric of the correlation coefficient values isless than a threshold level.
 7. The device of claim 1, wherein the firstbin parameter comprises a first width, wherein the second bin parametercomprises a second width, wherein each width of the first width and thesecond width is defined by a difference of a respective minimum value ofthe first physiological parameter and a respective maximum value of thefirst physiological parameter, and wherein the processing circuitry isconfigured to determine the metric of the correlation coefficient valuesat least in part by determining the metric of the correlationcoefficient values associated with values of the first physiologicalparameter in a range of greater than the respective minimum value andless than the respective maximum value.
 8. The device of claim 1,wherein the metric of the correlation coefficient values comprises amedian of the correlation coefficient values within the respective binof the set of bins.
 9. The device of claim 1, wherein the metric of thecorrelation coefficient values comprises a mean of the correlationcoefficient values within the respective bin of the set of bins.
 10. Thedevice of claim 1, wherein the first bin parameter comprises a firstwidth, wherein the second bin parameter comprises a second width, andwherein at least one of the first width or the second width is less thanor equal to four millimeters of mercury (mmHg).
 11. The device of claim10, wherein at least one of the first width or the second width is in arange of greater than or equal to one mmHg and less than or equal tothree mmHg.
 12. The device of claim 1, wherein the first bin parametercomprises a first distance by which a center of each bin of the firstplurality of bins is offset from a center of an adjacent bin of thefirst plurality of bins, and wherein the second bin parameter comprisesa second distance by which a center of each bin of the second pluralityof bins is offset from a center of an adjacent bin of the secondplurality of bins, the second distance being different than the firstdistance.
 13. The device of claim 12, wherein at least one of the firstdistance or the second distance is less than or equal to fourmillimeters of mercury mmHg.
 14. The device of claim 12, wherein atleast one of the first distance or the second distance is in a range ofgreater than or equal to one millimeters of mercury (mmHg) and less thanor equal to three mmHg.
 15. The device of claim 1, wherein theprocessing circuitry is configured to determine the metric of thecorrelation coefficient values for each respective bin of the firstplurality of bins at least in part by: determining a first mean and astandard deviation of the correlation coefficient values for therespective bin; determining an outlier correlation coefficient that isgreater than three times the standard deviation from the first mean; anddetermining a second mean of the correlation coefficient values,excluding the outlier correlation coefficient, for the respective bin.16. A method comprising: receiving, by processing circuitry of a deviceand from sensing circuitry of the device, a first signal indicative of afirst physiological parameter of a patient; receiving, by the processingcircuitry and from the sensing circuitry, a second signal indicative ofa second physiological parameter of the patient; determining, by theprocessing circuitry, a set of correlation coefficient values for a setof values of the first physiological parameter and for a set of valuesof the second physiological parameter; determining, by the processingcircuitry, a metric of the correlation coefficient values for each binof a first plurality of bins, wherein each bin of the first plurality ofbins has a first bin parameter defined in terms of the firstphysiological parameter; determining, by the processing circuitry, ametric of the correlation coefficient values for each bin of a secondplurality of bins, wherein each bin of the second plurality of bins hasa second bin parameter defined in terms of the first physiologicalparameter, the second bin parameter being different than the first binparameter; determining, by the processing circuitry, a compositeestimate of a limit of autoregulation of the patient based on the metricof the correlation coefficient values for the first plurality of binsand the metric of the correlation coefficient values for the secondplurality of bins; determining, by the processing circuitry, anautoregulation status of the patient based on the composite estimate ofthe limit of autoregulation; and outputting, by the processing circuitryfor display, an indication of the autoregulation status.
 17. A devicecomprising: a display; and processing circuitry configured to: receive afirst signal indicative of a first physiological parameter of a patient;receive a second signal indicative of a second physiological parameterof the patient; determine a set of correlation coefficient values for aset of values of the first physiological parameter and for a set ofvalues of the second physiological parameter; determine a metric of thecorrelation coefficient values for each bin of a first plurality of binsat least in part by determining a first weighting factor for eachcorrelation coefficient, wherein each bin of the first plurality of binshas a first bin parameter defined in terms of the first physiologicalparameter; determine the metric of the correlation coefficient valuesfor each bin of a second plurality of bins at least in part bydetermining a second weighting factor for each correlation coefficient,wherein each bin of the second plurality of bins has a second binparameter defined in terms of the first physiological parameter, thesecond bin parameter being different than the first bin parameter;determine a composite estimate of a limit of autoregulation of thepatient based on the metric of the correlation coefficient values forthe first plurality of bins and the metric of the correlationcoefficient values for the second plurality of bins; determine anautoregulation status of the patient based on the composite estimate ofthe limit of autoregulation; and output, for display via the display, anindication of the autoregulation status.
 18. The device of claim 17,wherein the processing circuitry is configured to determine the firstweighting factor based on an age of each correlation coefficient withinthe respective bin such that more recent correlation coefficient valuesare weighted higher than less recent correlation coefficient values, andwherein the processing circuitry is configured to determine the secondweighting factor based on the age of each correlation coefficient withinthe respective bin such that more recent correlation coefficient valuesare weighted higher than less recent correlation coefficient values. 19.The device of claim 17, wherein the processing circuitry is configuredto determine the first weighting factor based on a distance between acenter of a bin of the first plurality of bins and a value of the firstphysiological parameter associated with the respective correlationcoefficient such that correlation coefficient values closer to thecenter of the bin are weighted higher than correlation coefficientvalues farther from the center of the bin, and wherein the processingcircuitry is configured to determine the second weighting factor basedon a distance between a center of a bin of the second plurality of binsand the value of the first physiological parameter associated with therespective correlation coefficient such that correlation coefficientvalues closer to the center of the bin are weighted higher thancorrelation coefficient values farther from the center of the bin. 20.The device of claim 17, wherein the processing circuitry is configuredto determine the first weighting factor based on a signal qualitymetric, and wherein the processing circuitry is configured to determinethe second weighting factor based on the signal quality metric.